An Introduction to Generalized Linear Models

Zusatztext Praise for the Third Edition:Overall! this new edition remains a highly useful and compact introduction to a large number of seemingly disparate regression models. Depending on the background of the audience! it will be suitable for upper-level undergraduate or beginning post-graduate courses.-Christian Kleiber! Statistical Papers (2012) 53The comments of Lang in his review of the second edition! that 'This relatively short book gives a nice introductory overview of the theory underlying generalized linear modelling. ?' can equally be applied to the new edition. ? three new chapters on Bayesian analysis are also added. ? suitable for experienced professionals needing to refresh their knowledge ? .-Pharmaceutical Statistics! 2011 The chapters are short and concise! and the writing is clear? explanations are fundamentally sound and aimed well at an upper-level undergrad or early graduate student in a statistics-related field. This is a very worthwhile book: a good class text and a practical reference for applied statisticians. -BiometricsThis book promises in its introductory section to provide a unifying framework for many statistical techniques. It accomplishes this goal easily. ? Furthermore! the text covers important topics that are frequently overlooked in introductory courses! such as models for ordinal outcomes. ? This book is an excellent resource! either as an introduction to or a reminder of the technical aspects of generalized linear models and provides a wealth of simple yet useful examples and data sets.-Journal of Biopharmaceutical Statistics! Issue 2This book aims to provide an overview of the key issues in generalized linear models (GLMs)! including assumptions! estimation methods! different link functions! and a Bayesian approach. Applications of the book concern different types of data! such as continuous! categorical! count! correlated! and time-to-event data. The book contains theoretical and applicable examples of different type of GLMs. The first five chapters introduce the basics of linear models and the relations between different distributions. The following chapters explain GLMs in respect to different types of link function. One of the most important features of the book is the statistical software codes in each chapter! which make it more practical! as well as the last chapter that focuses on examples of Bayesian analysis.- Morteza Hajihosseini in ISCB! June 2019 Informationen zum Autor Annette J. Dobson is Professor of Biostatistics at the Univesity of Queensland. Adrian G. Barnett is a professor at the Queensland University of Technology. Klappentext An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice. Zusammenfassung An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice. Inhaltsverzeichnis Introduction. Model Fitting. Exponential Family and Generalized. Linear Models.Estimation. Inference. Normal Linear Models. Binary Variables and Logistic Regression. Nominal and Ordinal Logistic Regression. Poisson Regression and Log-Linear Models.Survival Analysis. Clustered and Longitudinal Data. Bayesian Analysis. Markov Chain Monte Carlo Methods. Example Bayesian Analyses. Postface. Appendix. ...

Praise for the Third Edition: Overall, this new edition remains a highly useful and compact introduction to a large number of seemingly disparate regression models. Depending on the background of the audience, it will be suitable for upper-level undergraduate or beginning post-graduate courses.Christian Kleiber, Statistical Papers (2012) 53The comments of Lang in his review of the second edition, that 'This relatively short book gives a nice introductory overview of the theory underlying generalized linear modelling. ' can equally be applied to the new edition. three new chapters on Bayesian analysis are also added. suitable for experienced professionals needing to refresh their knowledge .Pharmaceutical Statistics, 2011 The chapters are short and concise, and the writing is clear explanations are fundamentally sound and aimed well at an upper-level undergrad or early graduate student in a statistics-related field. This is a very worthwhile book: a good class text and a practical reference for applied statisticians. Biometrics This book promises in its introductory section to provide a unifying framework for many statistical techniques. It accomplishes this goal easily. Furthermore, the text covers important topics that are frequently overlooked in introductory courses, such as models for ordinal outcomes. This book is an excellent resource, either as an introduction to or a reminder of the technical aspects of generalized linear models and provides a wealth of simple yet useful examples and data sets.Journal of Biopharmaceutical Statistics, Issue 2 This book aims to provide an overview of the key issues in generalized linear models (GLMs), including assumptions, estimation methods, different link functions, and a Bayesian approach. Applications of the book concern different types of data, such as continuous, categorical, count, correlated, and time-to-event data. The book contains theoretical and applicable examples of different type of GLMs. The first five chapters introduce the basics of linear models and the relations between different distributions. The following chapters explain GLMs in respect to different types of link function. One of the most important features of the book is the statistical software codes in each chapter, which make it more practical, as well as the last chapter that focuses on examples of Bayesian analysis.- Morteza Hajihosseini in ISCB, June 2019

Auteur

Annette J. Dobson is Professor of Biostatistics at the Univesity of Queensland.

Adrian G. Barnett is a professor at the Queensland University of Technology.

Texte du rabat

An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.

Résumé
An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.

Contenu

Introduction. Model Fitting. Exponential Family and Generalized. Linear Models.Estimation. Inference. Normal Linear Models. Binary Variables and Logistic Regression. Nominal and Ordinal Logistic Regression. Poisson Regression and Log-Linear Models.Survival Analysis. Clustered and Longitudinal Data. Bayesian Analysis. Markov Chain Monte Carlo Methods. Example Bayesian Analyses. Postface. Appendix.